Automated brain tumour detection and segmentation using superpixel-based extremely randomized trees in FLAIR MRI
File(s)
Published version
Author(s)
Type
Journal Article
Abstract
Purpose: We propose a fully automated method for detection and segmentation of the abnormal tissue associated with brain tumour (tumour core and oedema) from Fluid- Attenuated Inversion Recovery (FLAIR) Magnetic Resonance Imaging (MRI). Methods: The method is based on superpixel technique and classification of each superpixel. A number of novel image features including intensity-based, Gabor textons, fractal analysis and curvatures are calculated from each superpixel within the entire brain area in FLAIR MRI to ensure a robust classification. Extremely randomized trees (ERT) classifier is compared with support vector machine (SVM) to classify each superpixel into tumour and non-tumour. Results: The proposed method is evaluated on two datasets: (1) Our own clinical dataset: 19 MRI FLAIR images of patients with gliomas of grade II to IV, and (2) BRATS 2012 dataset: 30 FLAIR images with 10 low-grade and 20 high-grade gliomas. The experimental results demonstrate the high detection and segmentation performance of the proposed method using ERT classifier. For our own cohort, the average detection sensitivity, balanced error rate and the Dice overlap measure for the segmented tumour against the ground truth are 89.48 %, 6 % and 0.91, respectively, while, for the BRATS dataset, the corresponding evaluation results are 88.09 %, 6 % and 0.88, respectively. Conclusions: This provides a close match to expert delineation across all grades of glioma, leading to a faster and more reproducible method of brain tumour detection and delineation to aid patient management.
Date Issued
2016-09-20
Date Acceptance
2016-08-31
Citation
International Journal of Computer Assisted Radiology and Surgery, 2016, 12 (2), pp.183-203
ISSN
1861-6410
Publisher
Springer
Start Page
183
End Page
203
Journal / Book Title
International Journal of Computer Assisted Radiology and Surgery
Volume
12
Issue
2
Copyright Statement
© 2016 The Author(s). This article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made.
Subjects
Brain tumour segmentation
Extremely randomized trees
Feature selection
Magnetic resonance imaging
Superpixels
Textons
Adult
Aged
Brain
Brain Neoplasms
Female
Glioma
Humans
Image Processing, Computer-Assisted
Magnetic Resonance Imaging
Male
Middle Aged
Reproducibility of Results
Support Vector Machine
Young Adult
Nuclear Medicine & Medical Imaging
1103 Clinical Sciences
Publication Status
Published